IEEE Trans Med Imaging. 2023 Feb;42(2):354-367. doi: 10.1109/TMI.2022.3187141. Epub 2023 Feb 2.
For significant memory concern (SMC) and mild cognitive impairment (MCI), their classification performance is limited by confounding features, diverse imaging protocols, and limited sample size. To address the above limitations, we introduce a dual-modality fused brain connectivity network combining resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), and propose three mechanisms in the current graph convolutional network (GCN) to improve classifier performance. First, we introduce a DTI-strength penalty term for constructing functional connectivity networks. Stronger structural connectivity and bigger structural strength diversity between groups provide a higher opportunity for retaining connectivity information. Second, a multi-center attention graph with each node representing a subject is proposed to consider the influence of data source, gender, acquisition equipment, and disease status of those training samples in GCN. The attention mechanism captures their different impacts on edge weights. Third, we propose a multi-channel mechanism to improve filter performance, assigning different filters to features based on feature statistics. Applying those nodes with low-quality features to perform convolution would also deteriorate filter performance. Therefore, we further propose a pooling mechanism, which introduces the disease status information of those training samples to evaluate the quality of nodes. Finally, we obtain the final classification results by inputting the multi-center attention graph into the multi-channel pooling GCN. The proposed method is tested on three datasets (i.e., an ADNI 2 dataset, an ADNI 3 dataset, and an in-house dataset). Experimental results indicate that the proposed method is effective and superior to other related algorithms, with a mean classification accuracy of 93.05% in our binary classification tasks. Our code is available at: https://github.com/Xuegang-S.
对于显著记忆关注（SMC）和轻度认知障碍（MCI），其分类性能受到混杂特征、不同成像协议和有限样本量的限制。为了解决上述限制，我们引入了一种结合静息态功能磁共振成像（fMRI）和弥散张量成像（DTI）的双模融合脑连接网络，并在当前图卷积网络（GCN）中提出了三种机制来提高分类器性能。首先，我们引入了一个 DTI 强度惩罚项来构建功能连接网络。更强的结构连接和更大的组间结构强度多样性为保留连接信息提供了更高的机会。其次，提出了一种具有每个节点代表一个主体的多中心注意图，以考虑 GCN 中训练样本的数据源、性别、采集设备和疾病状态的影响。注意机制捕获它们对边缘权重的不同影响。第三，我们提出了一种多通道机制来提高滤波器性能，根据特征统计为特征分配不同的滤波器。将那些具有低质量特征的节点应用于卷积也会降低滤波器性能。因此，我们进一步提出了一种池化机制，它引入了训练样本的疾病状态信息来评估节点的质量。最后，我们通过将多中心注意图输入到多通道池化 GCN 中来获得最终的分类结果。该方法在三个数据集（即 ADNI 2 数据集、ADNI 3 数据集和内部数据集）上进行了测试。实验结果表明，该方法是有效的，优于其他相关算法，在我们的二分类任务中平均分类准确率为 93.05%。我们的代码可在：https://github.com/Xuegang-S.
